{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Applying Interoperable Machine Learning in StreamPipes\n",
    "\n",
    "The last tutorial ([Using Online Machine Learning on a StreamPipes data stream](../4-using-online-machine-learning-on-a-streampipes-data-stream)) demonstrated how patterns in streaming data can be learned online. In contrast, this tutorial demonstrates how one can apply a pre-trained machine learning (ML) model to a StreamPipes data stream making use of [ONNX](https://onnx.ai/). We will show how StreamPipes can be used for both: extracting historical data for training purposes and using model inference on live data with a pre-trained model."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "7b5d1600cbd639f4"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Preparation\n",
    "\n",
    "The following lines configure the client and establish a connection to the StreamPipes instance. If you're not familiar with it or anything is unclear, please have a look at our [first tutorial](../1-introduction-to-streampipes-python-client)."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "8e7886c9f47869b7"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "%pip install git+https://github.com/apache/streampipes.git#subdirectory=streampipes-client-python\n",
    "%pip install scikit-learn==1.4.0 skl2onnx==1.16.0 onnxruntime==1.17.1 matplotlib==3.8.3"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "90863de38bbbd7cb",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-03-26 10:21:38,538 - streampipes.client.client - [INFO] - [client.py:198] [_set_up_logging] - Logging successfully initialized with logging level INFO.\n",
      "2024-03-26 10:21:38,632 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:164] [_make_request] - Successfully retrieved all resources.\n",
      "2024-03-26 10:21:38,634 - streampipes.client.client - [INFO] - [client.py:171] [_get_server_version] - The StreamPipes version was successfully retrieved from the backend: 0.95.0. By means of that, authentication via the provided credentials is also tested successfully.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from streampipes.client import StreamPipesClient\n",
    "from streampipes.client.config import StreamPipesClientConfig\n",
    "from streampipes.client.credential_provider import StreamPipesApiKeyCredentials\n",
    "\n",
    "os.environ[\"BROKER-HOST\"] = \"localhost\"\n",
    "os.environ[\"KAFKA-PORT\"] = \"9094\"  # When using Kafka as message broker\n",
    "\n",
    "config = StreamPipesClientConfig(\n",
    "    credential_provider=StreamPipesApiKeyCredentials(\n",
    "        username=\"admin@streampipes.apache.org\",\n",
    "        api_key=\"TOKEN\",\n",
    "    ),\n",
    "    host_address=\"localhost\",\n",
    "    https_disabled=True,\n",
    "    port=80\n",
    ")\n",
    "\n",
    "client = StreamPipesClient(client_config=config)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-26T09:21:38.636384Z",
     "start_time": "2024-03-26T09:21:38.524284Z"
    }
   },
   "id": "f6914b8d5f32b4b2",
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "source": [
    "The main objective of this tutorial is to demonstrate how to make predictions with an existing and pre-trained ML model using a StreamPipes function and ONNX. Therefore, you can skip the following sections on use case and model training if you already have an existing ONNX model and are only interested in applying it using StreamPipes."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "bf224dcb823c0252"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Machine Learning Use Case \n",
    "\n",
    "In this tutorial, we will use data generated by the [Machine Data Simulator](https://streampipes.apache.org/docs/pe/org.apache.streampipes.connect.iiot.adapters.simulator.machine/) adapter. More specifically, we will focus on the `flowrate` data, which consists of various sensor values coming from a water pipe system. Our goal is keep an eye on the parameter `volume_flow`, which represents the current volume flow in cubic meters/second. For this parameter, we want to detect anomalies that could indicate problems such as leaks, blockages, etc.\n",
    "\n",
    "To get the concerned data, we simply need to create an instance of the machine data simulator and persist the data in the data lake:\n",
    "\n",
    "</br>\n",
    "\n",
    "![tutorial-preparation](https://raw.githubusercontent.com/apache/streampipes/dev/streampipes-client-python/docs/img/tutorial-preparation.gif)\n",
    "\n",
    "</br>\n",
    "\n",
    "If you choose to perform the model training step yourself, you will need to wait approximately 15 minutes for enough data to be available for model training. If you want to speed this up, you can configure a lower wait time when creating the adapter. Please be aware that this also influences the inference scenario."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a08412c577712afa"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Model Training with Historic Data\n",
    "\n",
    "As said above, the aim of our model is to detect anomalies of the `volume_flow` parameter. For this task, we will use [Isolation Forests](https://en.wikipedia.org/wiki/Isolation_forest).\n",
    "Please note that the focus of the tutorial is not on training the model, so please be patient even though the training is very simplified and lacks important preparation steps such as standardization.\n",
    "\n",
    "As a first step, lets query the `flowrate` data from the StreamPipes data lake and extract the values of `volume_flow` as a feature:"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5a46240838b5b86a"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-03-26 10:21:48,582 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:164] [_make_request] - Successfully retrieved all resources.\n"
     ]
    }
   ],
   "source": [
    "flowrate_df = client.dataLakeMeasureApi.get(\"flow-rate\").to_pandas()\n",
    "X = flowrate_df[\"volume_flow\"].values.reshape(-1, 1).astype(\"float32\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-26T09:21:48.637328Z",
     "start_time": "2024-03-26T09:21:48.367842Z"
    }
   },
   "id": "2ebb7a210fc2d9d8",
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "source": [
    "Let's fit the model to the data:"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "af336a5796303eac"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "IsolationForest(contamination=0.01)",
      "text/html": "<style>#sk-container-id-3 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-3 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-3 pre {\n  padding: 0;\n}\n\n#sk-container-id-3 input.sk-hidden--visually {\n  border: 0;\n  clip: rect(1px 1px 1px 1px);\n  clip: rect(1px, 1px, 1px, 1px);\n  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\n#sk-container-id-3 div.sk-dashed-wrapped {\n  border: 1px dashed var(--sklearn-color-line);\n  margin: 0 0.4em 0.5em 0.4em;\n  box-sizing: border-box;\n  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-3 div.sk-container {\n  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n     but bootstrap.min.css set `[hidden] { display: none !important; }`\n     so we also need the `!important` here to be able to override the\n     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-3 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-3 div.sk-parallel-item::after {\n  content: \"\";\n  width: 100%;\n  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n  flex-grow: 1;\n}\n\n#sk-container-id-3 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-3 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-3 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-3 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-3 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-3 div.sk-serial {\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-3 div.sk-toggleable {\n  /* Default theme specific background. It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-3 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-3 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-3 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-3 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n#sk-container-id-3 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-3 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-3 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-3 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-3 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-3 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-3 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-3 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-3 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-3 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>IsolationForest(contamination=0.01)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;IsolationForest<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.IsolationForest.html\">?<span>Documentation for IsolationForest</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>IsolationForest(contamination=0.01)</pre></div> </div></div></div></div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import IsolationForest\n",
    "\n",
    "model = IsolationForest(contamination=0.01)\n",
    "model.fit(X)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-26T09:22:39.109568Z",
     "start_time": "2024-03-26T09:22:39.016690Z"
    }
   },
   "id": "993655af84aea201",
   "execution_count": 14
  },
  {
   "cell_type": "markdown",
   "source": [
    "The `contamination` parameter models the proportion of outliers in the data. See the [scikit-learn documentation](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html) for more information."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c7e9ab3695506bc6"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Before we convert the model to an ONNX representation, let's do a quick visual analysis of the model results:"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "3a2a95861654a640"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "flowrate_df[\"anomaly\"] = model.predict(X)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "anomalies = flowrate_df.loc[flowrate_df[\"anomaly\"] == -1, [\"volume_flow\"]]\n",
    "ax.plot(flowrate_df.index, flowrate_df['volume_flow'], color='black', label='volume_flow')\n",
    "ax.scatter(anomalies.index, anomalies['volume_flow'], color='red', label='Anomaly')\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-26T09:22:40.358760Z",
     "start_time": "2024-03-26T09:22:40.224423Z"
    }
   },
   "id": "ec63685dad8b685e",
   "execution_count": 15
  },
  {
   "cell_type": "markdown",
   "source": [
    "Okay, that looks quite reasonable so lets covert the model to an ONNX representation so that we can make use of it later."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "9a63a058b00101d7"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "from onnxconverter_common import FloatTensorType\n",
    "from skl2onnx import to_onnx\n",
    "\n",
    "model_onnx = to_onnx(\n",
    "    model,\n",
    "    initial_types=[('input', FloatTensorType([None, X.shape[1]]))],\n",
    "    target_opset={'ai.onnx.ml': 3, 'ai.onnx': 15, '': 15}\n",
    ")\n",
    "\n",
    "with open(\"isolation_forest.onnx\", \"wb\") as f:\n",
    "    f.write(model_onnx.SerializeToString())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-26T09:25:42.244773Z",
     "start_time": "2024-03-26T09:25:36.648160Z"
    }
   },
   "id": "5a4b088b556e3950",
   "execution_count": 16
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Model Inference with Live Data\n",
    "\n",
    "Utilizing a pre-trained model within StreamPipes becomes seamless with the ONNX interoperability standard, enabling effortless application of your existing model on live data streams.\n",
    "\n",
    "Interacting with live data from StreamPipes is facilitated through StreamPipes functions. Below, we'll create a Python StreamPipes function that leverages an ONNX model to generate predictions for each incoming event, making the results accessible as a data stream within StreamPipes for subsequent steps.\n",
    "\n",
    "So let's create an `ONNXFunction` that is capable of applying a model in ONNX representation to a StreamPipes data stream. If you'd like to read more details about how functions are defined, refer to [our third tutorial](../3-getting-live-data-from-the-streampipes-data-stream)."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e280a9751ba0438e"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import onnxruntime as rt\n",
    "\n",
    "from streampipes.functions.broker.broker_handler import get_broker_description\n",
    "from streampipes.functions.streampipes_function import StreamPipesFunction\n",
    "from streampipes.functions.utils.data_stream_generator import create_data_stream, RuntimeType\n",
    "from streampipes.functions.utils.function_context import FunctionContext\n",
    "from streampipes.model.resource import FunctionDefinition, DataStream\n",
    "\n",
    "from typing import Dict, Any, List\n",
    "\n",
    "\n",
    "class ONNXFunction(StreamPipesFunction):\n",
    "\n",
    "    def __init__(self, feature_names: list[str], input_stream: DataStream):\n",
    "        output_stream = create_data_stream(\n",
    "            name=\"flowrate-prediction\",\n",
    "            attributes={\n",
    "                \"is_anomaly\": RuntimeType.BOOLEAN.value\n",
    "            },\n",
    "            broker=get_broker_description(input_stream)\n",
    "        )\n",
    "\n",
    "        function_definition = FunctionDefinition(\n",
    "            consumed_streams=[input_stream.element_id]\n",
    "        ).add_output_data_stream(output_stream)\n",
    "\n",
    "        self.feature_names = feature_names\n",
    "        self.input_name = None\n",
    "        self.output_name = None\n",
    "        self.session = None\n",
    "\n",
    "        super().__init__(function_definition=function_definition)\n",
    "\n",
    "    def onServiceStarted(self, context: FunctionContext) -> None:\n",
    "        self.session = rt.InferenceSession(\n",
    "            path_or_bytes=\"isolation_forest.onnx\",\n",
    "            providers=rt.get_available_providers(),\n",
    "        )\n",
    "        self.input_name = self.session.get_inputs()[0].name\n",
    "        self.output_name = self.session.get_outputs()[0].name\n",
    "\n",
    "    def onEvent(self, event: Dict[str, Any], streamId: str) -> None:\n",
    "        feature_vector = []\n",
    "        for feature in self.feature_names:\n",
    "            feature_vector.append(event[feature])\n",
    "\n",
    "        prediction = self.session.run(\n",
    "            [self.output_name],\n",
    "            {self.input_name: np.expand_dims(np.array(feature_vector), axis=0).astype(\"float32\")}\n",
    "        )[0]\n",
    "\n",
    "        output = {\n",
    "            \"is_anomaly\": int(prediction[0]) == -1\n",
    "        }\n",
    "\n",
    "        self.add_output(\n",
    "            stream_id=self.function_definition.get_output_stream_ids()[0],\n",
    "            event=output\n",
    "        )\n",
    "\n",
    "    def onServiceStopped(self) -> None:\n",
    "        pass"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-26T11:38:38.601489Z",
     "start_time": "2024-03-26T11:38:38.575652Z"
    }
   },
   "id": "248725a8e04c5adb",
   "execution_count": 23
  },
  {
   "cell_type": "markdown",
   "source": [
    "Let's dive a little deeper into the different parts of the function\n",
    "\n",
    "- **`__init__`**: First, we need to take care of the data stream that is required to send the predictions from our function to StreamPipes. Thus, we create a dedicated output data stream which we need to provide with the attributes our event will consist of (a timestamp attribute is always added automatically). This output data stream needs to be registered at the function definition which is to be passed to the parent class. Lastly, we need to define some instance variables that are mainly required for the ONNX runtime.\n",
    "\n",
    "- **`onServiceStarted`**: Here we prepare the ONNX runtime session by creating an `InferenceSession` and retrieving the corresponding configuration parameters.\n",
    "\n",
    "- **`onEvent`**: Following the parameter names specified by `self.feature_names`, we extract all feature values from the current event. Subsequently, the corresponding feature vector is transmitted to the ONNX runtime session. The resulting prediction is then converted into our output event, where a value of `-1` signifies an anomaly. Finally, the generated output event is forwarded to StreamPipes.\n",
    "\n",
    "\n",
    "Having the function code in place, we can start the function with the following:"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "b160f88e67eddde2"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-03-26 12:39:50,443 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:164] [_make_request] - Successfully retrieved all resources.\n",
      "2024-03-26 12:39:50,502 - streampipes.functions.function_handler - [INFO] - [function_handler.py:76] [initializeFunctions] - The data stream could not be created.\n",
      "2024-03-26 12:39:50,503 - streampipes.functions.function_handler - [INFO] - [function_handler.py:78] [initializeFunctions] - This is due to the fact that this data stream already exists. Continuing with the existing data stream.\n",
      "2024-03-26 12:39:50,503 - streampipes.functions.function_handler - [INFO] - [function_handler.py:84] [initializeFunctions] - Using output data stream 'sp:spdatastream:flowrate-prediction' for function '7c06fa31-9534-4f91-9c50-b7a3607ec3dc'\n",
      "2024-03-26 12:39:50,548 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:164] [_make_request] - Successfully retrieved all resources.\n",
      "2024-03-26 12:39:50,549 - streampipes.functions.function_handler - [INFO] - [function_handler.py:100] [initializeFunctions] - Using KafkaConsumer for ONNXFunction\n"
     ]
    }
   ],
   "source": [
    "from streampipes.functions.registration import Registration\n",
    "from streampipes.functions.function_handler import FunctionHandler\n",
    "\n",
    "stream = [\n",
    "    stream\n",
    "    for stream\n",
    "    in client.dataStreamApi.all()\n",
    "    if stream.name == \"flow-rate\"\n",
    "][0]\n",
    "\n",
    "function = ONNXFunction(\n",
    "    feature_names=[\"volume_flow\"],\n",
    "    input_stream=stream\n",
    ")\n",
    "\n",
    "registration = Registration()\n",
    "registration.register(function)\n",
    "function_handler = FunctionHandler(registration, client)\n",
    "function_handler.initializeFunctions()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-26T11:39:50.550688Z",
     "start_time": "2024-03-26T11:39:50.347386Z"
    }
   },
   "id": "2705486ed1c8cf75",
   "execution_count": 25
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can now access the live values of the prediction in the StreamPipes UI, e.g., in the pipeline editor.\n",
    "\n",
    "</br>\n",
    "\n",
    "![prediction-data-stream](https://raw.githubusercontent.com/apache/streampipes/dev/streampipes-client-python/docs/img/tutorial-prediction-data-stream.png)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ee7405a4fdc2ef7e"
  },
  {
   "cell_type": "markdown",
   "source": [
    "That's already it. We hope this tutorial serves as an illustration how ML models can be utilized in StreamPipes with the help of ONNX."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "257874d0b9f060e7"
  },
  {
   "cell_type": "markdown",
   "source": [
    "How do you like this tutorial? We hope you like it and would love to receive some feedback from you. Just go to our GitHub discussion page and let us know your impression. We'll read and react to them all, we promise!"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "44d17b02f414d985"
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   "source": [],
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